The SAR Model for Very Large Datasets: A Reduced Rank Approach
AbstractThe SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random effects (SRE) model, and in this article, we calibrate it to the SAR model of interest using a generalisation of the Moran operator that allows for heteroskedasticity and an asymmetric SAR spatial dependence matrix. In general, spatial data have a measurement-error component, which we model, and we use restricted maximum likelihood to estimate the SRE model covariance parameters; its required computational time is only the order of the size of the dataset. Our implementation is demonstrated using mean usual weekly income data from the 2011 Australian Census. View Full-Text
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Burden, S.; Cressie, N.; Steel, D.G. The SAR Model for Very Large Datasets: A Reduced Rank Approach. Econometrics 2015, 3, 317-338.
Burden S, Cressie N, Steel DG. The SAR Model for Very Large Datasets: A Reduced Rank Approach. Econometrics. 2015; 3(2):317-338.Chicago/Turabian Style
Burden, Sandy; Cressie, Noel; Steel, David G. 2015. "The SAR Model for Very Large Datasets: A Reduced Rank Approach." Econometrics 3, no. 2: 317-338.